Optimal Energy Management for Off-Grid PV-Battery Systems

Abstract

This present work introduces an advanced energy management approach for a photovoltaic-battery system supplying a DC load under varying irradiation, temperature, load, and battery charge level conditions. The suggested approach integrates an integral quasi sliding control whose parameters are tuned using the Bald Eagle Search algorithm to improve system effectiveness. The energy management strategy ensures proper power distribution between the PV source, battery, and load while maintaining system stability. The adopted algorithm optimally tunes the control coefficients by minimizing a fitness function. MATLAB/Simulink-based simulation results indicate that the BES-IQSMC strategy provides batter performance than conventional SMC and BES-SMC approaches regarding tracking accuracy, reduced oscillations, dynamic performance, and disturbance tolerance. In addition, the proposed controller reduces the fitness value to 3.26 × 10 −3 with error indices of ISE = 8.15 × 10 −4 , IAE = 10 −3 , and ITAE = 1.6 × 10 −2 and smoother current tracking, demonstrating superior dynamic performance and enhanced stability under variable operating conditions.